研究目的
Investigating the identification of target aspects from SAR images using a machine learning approach to improve accuracy and efficiency.
研究成果
The proposed method demonstrates superior performance in SAR target aspect identification, both qualitatively and quantitatively, compared to traditional methods, with potential applications in SAR automatic target recognition systems and battlefield environment assessment.
研究不足
The method requires image pre-processing and may be influenced by the mutability of target appearance, speckle, and shadow in SAR images.
1：Experimental Design and Method Selection:
The study employs a machine learning approach involving subspace aspect discriminant analysis and a multi-layer neural network for SAR target aspect identification.
2：Sample Selection and Data Sources:
Utilizes SAR images from the MSTAR dataset, with images at depression angles of 17° for training and 15° for testing.
3：List of Experimental Equipment and Materials:
Uses SAR images with a resolution of
4：3m×3m, processed with gray enhancement based on the power function. Experimental Procedures and Operational Workflow:
Discretizes aspect angles, establishes spatial relationships, projects samples into a low-dimensional space, and uses a neural network for aspect identification.
5：Data Analysis Methods:
Evaluates performance using mean absolute difference (MAD) of aspect identification.
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